Benchmarking Scientific Machine Learning Models for Air Quality Data

ArXi:2603.21039v1 Announce Type: new Accurate air quality index (AQI) forecasting is essential for the protecting public health in rapidly growing urban regions, and the practical model evaluation and selection are often challenged by the lack of rigorous, region-specific benchmarking on standardized datasets. Physics-guided machine learning and deep learning models could be a good and effective solution to resolve such issues with accurate and efficient AQI forecasting.